A framework for knowledge – Driven CRM

In this paper we propose a framework to combine KDD (Knowledge Discovered in Databases) and CRM (Customer Relationship Management), with an emphasis on customer retention. The key aspect of the proposed framework is to enable adaptive use of knowledge discovered to predict customer buying patterns and capture interesting knowledge about customers

Customer Relationship Management : Concept, Strategy, and Tools -3/E

Customer relationship management (CRM) as a strategy and as a technology has gone through an amazing evolutionary journey. After the initial technological approaches, this process has matured considerably – both from a conceptual and from an applications point of view. Of course this evolution continues, especially in the light of the digital transformation. Today, CRM refers to a strategy, a set of tactics, and a technology that has become indispensable in the modern economy. Based on both authors’ rich academic and managerial experience, this book gives a unified treatment of the strategic and tactical aspects of customer relationship management as we know it today. It stresses developing an understanding of economic customer value as the guiding concept for marketing decisions. The goal of this book is to be a comprehensive and up-to-date learning companion for advanced undergraduate students, master students, and executives who want a detailed and conceptually sound insight into the field of CRM

Customer lifetime value : an integrated data mining approach

Customer Lifetime Value (CLV) ---which is a measure of the profit generating potential, or value, of a customer---is increasingly being considered a touchstone for customer relationship management. As the guide and benchmark for Customer Relationship Management (CRM) applications, CLV analysis has received increasing attention from both the marketing practitioners and researchers from different domains. Furthermore, the central challenge in predicting CLV is the precise calculation of customer’s length of service (LOS). There are several statistical approaches for this problem and several researchers have used these approaches to perform survival analysis in different domains. However, classical survival analysis techniques like Kaplan-Meier approach which offers a fully non-parametric estimate ignores the covariates completely and assumes stationary of churn behavior along time, which makes it less practical. Further, segments of customers, whose lifetimes and covariate effects can vary widely, are not necessarily easy to detect. Like many other applications, data mining is emerging as a compelling analysis tool for the CLV application recently. Comparatively, data mining methods offer an interesting alternative with the fact that they are less limited than the conventional statistical approaches. Customer databases contain histories of vital events such as the acquisition and cancellation of products and services. The historical data is used to build predictive models for customer retention, cross-selling, and other database marketing endeavors. In this research project we discuss and investigate the possibility of combining these statistical approaches with data mining methods to improve the performance for the CLV problem in a real business context. Part of the research effort is placed on the precise prediction of LOS of the customers in concentration of a real world business. Using the conventional statistical approaches and data mining methods in tandem, we demonstrate how data mining tools can be apt complements of the classical statistical models ---resulting in a CLV prediction model that is both accurate and understandable. We also evaluate the proposed integrated method to extract interesting business domain knowledge within the scope of CLV problem. In particular, several data mining methods are discussed and evaluated according to their accuracy of prediction and interpretability of results. The research findings will lead us to a data mining method combined with survival analysis approaches as a robust tool for modeling CLV and for assisting management decision-making. A calling plan strategy is designed based on the predicted survival time and calculated CLV for the telecommunication industry. The calling plan strategy further investigates potential business knowledge assisted by the CLV calculated

Further Thoughts on CRM

Skepticism and disappointment have replaced the initialenthusiasm about CRM. The disappointing results ofCRM-projects are often related to difficulties thatmanagers encounter in embedding CRM in their strategyand organization structure. In this article we presenta classification scheme on how CRM can be strategicallyembedded in organizations using the value disciplinesof Treacy and Wiersema. We use the findings from threecase studies to illustrate our classification. Based onthese case studies and interviews with managers wedistinguish between strategic and tactical CRM, andderive important issues that managers should considerbefore successfully implementing CRM.customer relationship management;marketing strategy;marketing performance

LEVERAGING SOCIAL NETWORK DATA FOR ANALYTICAL CRM STRATEGIES - THE INTRODUCTION OF SOCIAL BI

The skyrocketing trend for social media on the Internet greatly alters analytical Customer Relationship Management (CRM). Against this backdrop, the purpose of this paper is to advance the conceptual design of Business Intelligence (BI) systems with data identified from social networks. We develop an integrated social network data model, based on an in-depth analysis of Facebook. The data model can inform the design of data warehouses in order to offer new opportunities for CRM analyses, leading to a more consistent and richer picture of customers? characteristics, needs, wants, and demands. Four major contributions are offered. First, Social CRM and Social BI are introduced as emerging fields of research. Second, we develop a conceptual data model to identify and systematize the data available on online social networks. Third, based on the identified data, we design a multidimensional data model as an early contribution to the conceptual design of Social BI systems and demonstrate its application by developing management reports in a retail scenario. Fourth, intellectual challenges for advancing Social CRM and Social BI are discussed

Profiling for profit : a report on target marketing and profiling practices in the credit industry

This report examines how many businesses make significant investments to purchase and develop customer relationship management systems. Given such investments, information about these systems is not widely available, but some publicly available information gives indication of the extent, and purpose, of the use. Recognising that lenders use customer information and highly sophisticated systems to target their marketing strategies, is the first step towards ensuring that these practices are taken into account in the development of consumer policy and law reform. This research was funded by the consumer advisory panel of the Australian Securities and Investment Commission (ASIC)